Question

我有一个数据集，其中包含两列具有冻结集的列。现在，我想合并/合并这些frozensets。我可以使用for循环执行此操作，但是我的数据集包含> 2700万行，因此我正在寻找避免for循环的方法。任何想法吗？

数据

import pandas as pd
import numpy as np
d = {'ID1': [frozenset(['a', 'b']), frozenset(['a','c']), frozenset(['c','d'])],
    'ID2': [frozenset(['c', 'g']), frozenset(['i','f']), frozenset(['t','l'])]}
df = pd.DataFrame(data=d)

带有for循环的代码

from functools import reduce
df['frozenset']=0
for i in range(len(df)):
    df['frozenset'].iloc[i] = reduce(frozenset.union, [df['ID1'][i],df['ID2'][i]])

所需的输出

    ID1      ID2     frozenset
0   (a, b)  (c, g)  (a, c, g, b)
1   (a, c)  (f, i)  (a, c, f, i)
2   (c, d)  (t, l)  (c, d, t, l)

Answer 1

您似乎不需要在这里使用functools.reduce。对每对冷冻锅进行直接结合就足够了。

如果您希望尽可能快地进行此类操作，建议您查看列表推导内容（有关详细讨论，请参见For loops with pandas - When should I care?）。

df['union'] = [x | y for x, y in zip(df['ID1'], df['ID2'])]
df

      ID1     ID2         union
0  (a, b)  (c, g)  (c, a, b, g)
1  (c, a)  (f, i)  (c, a, i, f)
2  (c, d)  (l, t)  (c, l, d, t)

如果您希望将其概括为多列，则可以使用frozenset.union()将它们全部合并。

df['union2'] = [frozenset.union(*X) for X in df[['ID1', 'ID2']].values]
df

      ID1     ID2         union        union2
0  (a, b)  (c, g)  (c, a, b, g)  (c, a, b, g)
1  (c, a)  (f, i)  (c, a, i, f)  (c, a, i, f)
2  (c, d)  (l, t)  (c, l, d, t)  (c, l, d, t)

Answer 2

您可以尝试：

import pandas as pd
import numpy as np

d = {'ID1': [frozenset(['a', 'b']), frozenset(['a','c']), frozenset(['c','d'])],
    'ID2': [frozenset(['c', 'g']), frozenset(['i','f']), frozenset(['t','l'])]}
df = pd.DataFrame(data=d)
from functools import reduce
df['frozenset']=0

add = []
for i in range(len(df)):
    df['frozenset'].iloc[i] = reduce(frozenset.union, [df['ID1'][i],df['ID2'][i]])
add.append(df)
print(add)

两列的Frozenset联合

2 个答案: